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Transfer learning based multi-layer extreme learning machine for probabilistic wind power forecasting

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Cited by:

  1. Yuanzhuo Du & Kun Zhang & Qianzhi Shao & Zhe Chen, 2023. "A Short-Term Prediction Model of Wind Power with Outliers: An Integration of Long Short-Term Memory, Ensemble Empirical Mode Decomposition, and Sample Entropy," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
  2. Fu, Wenlong & Fu, Yuchen & Li, Bailing & Zhang, Hairong & Zhang, Xuanrui & Liu, Jiarui, 2023. "A compound framework incorporating improved outlier detection and correction, VMD, weight-based stacked generalization with enhanced DESMA for multi-step short-term wind speed forecasting," Applied Energy, Elsevier, vol. 348(C).
  3. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A temporal domain generalization approach incorporating hybrid model and adversarial relationship-based training," Applied Energy, Elsevier, vol. 355(C).
  4. Wang, Yuqing & Fu, Wenjie & Wang, Junlong & Zhen, Zhao & Wang, Fei, 2024. "Ultra-short-term distributed PV power forecasting for virtual power plant considering data-scarce scenarios," Applied Energy, Elsevier, vol. 373(C).
  5. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
  6. Henni, Sarah & Becker, Jonas & Staudt, Philipp & vom Scheidt, Frederik & Weinhardt, Christof, 2022. "Industrial peak shaving with battery storage using a probabilistic forecasting approach: Economic evaluation of risk attitude," Applied Energy, Elsevier, vol. 327(C).
  7. Tang, Yugui & Yang, Kuo & Zheng, Yichu & Ma, Li & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A transfer learning approach incorporating temporal convolution and adversarial training," Renewable Energy, Elsevier, vol. 224(C).
  8. Li, Yang & Wang, Shunli & Chen, Lei & Qi, Chuangshi & Fernandez, Carlos, 2023. "Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 282(C).
  9. Zhong, Mingwei & Xu, Cancheng & Xian, Zikang & He, Guanglin & Zhai, Yanpeng & Zhou, Yongwang & Fan, Jingmin, 2024. "DTTM: A deep temporal transfer model for ultra-short-term online wind power forecasting," Energy, Elsevier, vol. 286(C).
  10. Wiem Fekih Hassen & Maher Challouf, 2024. "Long Short-Term Renewable Energy Sources Prediction for Grid-Management Systems Based on Stacking Ensemble Model," Energies, MDPI, vol. 17(13), pages 1-19, June.
  11. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
  12. Rami Al-Hajj & Ali Assi & Bilel Neji & Raymond Ghandour & Zaher Al Barakeh, 2023. "Transfer Learning for Renewable Energy Systems: A Survey," Sustainability, MDPI, vol. 15(11), pages 1-28, June.
  13. Liu, Yanli & Wang, Junyi & Liu, Liqi, 2024. "Physics-informed reinforcement learning for probabilistic wind power forecasting under extreme events," Applied Energy, Elsevier, vol. 376(PA).
  14. Yang, Mao & Huang, Yutong & Xu, Chuanyu & Liu, Chenyu & Dai, Bozhi, 2025. "Review of several key processes in wind power forecasting: Mathematical formulations, scientific problems, and logical relations," Applied Energy, Elsevier, vol. 377(PC).
  15. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Huang, Yuehui & Chen, Jianjun & Li, Yahe & Chen, Zhe & Blaabjerg, Frede, 2024. "Bayesian averaging-enabled transfer learning method for probabilistic wind power forecasting of newly built wind farms," Applied Energy, Elsevier, vol. 355(C).
  16. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
  17. Dong, Xiaochong & Sun, Yingyun & Dong, Lei & Li, Jian & Li, Yan & Di, Lei, 2023. "Transferable wind power probabilistic forecasting based on multi-domain adversarial networks," Energy, Elsevier, vol. 285(C).
  18. Duan, Zhu & Liu, Hui & Li, Ye & Nikitas, Nikolaos, 2022. "Time-variant post-processing method for long-term numerical wind speed forecasts based on multi-region recurrent graph network," Energy, Elsevier, vol. 259(C).
  19. Xiong, Jinlin & Peng, Tian & Tao, Zihan & Zhang, Chu & Song, Shihao & Nazir, Muhammad Shahzad, 2023. "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Elsevier, vol. 266(C).
  20. Abdoos, Ali Akbar & Abdoos, Hatef & Kazemitabar, Javad & Mobashsher, Mohammad Mehdi & Khaloo, Hooman, 2023. "An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction," Energy, Elsevier, vol. 278(PA).
  21. Han, Shuo & He, Mengjiao & Zhao, Ziwen & Chen, Diyi & Xu, Beibei & Jurasz, Jakub & Liu, Fusheng & Zheng, Hongxi, 2023. "Overcoming the uncertainty and volatility of wind power: Day-ahead scheduling of hydro-wind hybrid power generation system by coordinating power regulation and frequency response flexibility," Applied Energy, Elsevier, vol. 333(C).
  22. Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2024. "A privacy-preserving framework integrating federated learning and transfer learning for wind power forecasting," Energy, Elsevier, vol. 286(C).
  23. Liu, Xin & Yang, Luoxiao & Zhang, Zijun, 2022. "The attention-assisted ordinary differential equation networks for short-term probabilistic wind power predictions," Applied Energy, Elsevier, vol. 324(C).
  24. Huang, Hao-Hsuan & Huang, Yun-Hsun, 2024. "Applying green learning to regional wind power prediction and fluctuation risk assessment," Energy, Elsevier, vol. 295(C).
  25. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2023. "Wind power forecasting: A hybrid forecasting model and multi-task learning-based framework," Energy, Elsevier, vol. 278(PA).
  26. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.
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